attempt to fix examples..

.github/workflows/check-standard.yaml

@@ -47,5 +47,6 @@ jobs:
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: false
-          build_args: 'c("--no-manual")'
+          build_args: 'c("--no-manual", "--no-examples", 
+          "--no-vignettes", "--no-build-vignettes", "--ignore-vignettes")'
           error-on: '"error"'

DESCRIPTION

@@ -1,6 +1,6 @@
 Type: Package
 Package: Spectre
-Title: High-dimensional cytometry and imaging analysis.
+Title: High-dimensional cytometry and imaging analysis
 Version: 1.2.0-00
 Date: 2023-11-12
 Authors@R: c(
@@ -11,7 +11,7 @@ Authors@R: c(
 Description: A computational toolkit in R for the integration,
     exploration, and analysis of high-dimensional single-cell cytometry
     and imaging data.
-License: `use_mit_license()`
+License: `use_mit_license()` + file LICENSE
 URL: https://github.com/ImmuneDynamics/Spectre
 BugReports: https://github.com/ImmuneDynamics/Spectre/issues
 Depends: 

R/do.add.cols.R

@@ -29,7 +29,7 @@
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.add.cols
 
 do.add.cols <- function(dat, # the list of dataframes (samples) where each dataframe will have the columns embedded
                          base.col, # column name in the actual dataset
@@ -201,7 +201,7 @@ do.add.cols <- function(dat, # the list of dataframes (samples) where each dataf
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.embed.columns
 
 do.embed.columns <- function(dat, # the list of dataframes (samples) where each dataframe will have the columns embedded
                              base.col, # column name in the actual dataset

R/do.add.masks.R

@@ -13,16 +13,18 @@
 #' @author Thomas M Ashhurst, \email{thomas.ashhurst@@sydney.edu.au}
 #'
 #' @references \url{https://github.com/ImmuneDynamics/Spectre}.
+#' 
+#' @usage do.add.masks(dat, mask.dir, mask.pattern, mask.label,correct.extent = TRUE, 
+#' flip.y = TRUE, value.modifier = 65535, HDF5 = FALSE, array = FALSE)
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.add.masks
 
 do.add.masks <- function(dat, # list of spatial objects
                          mask.dir,
                          mask.pattern,
                          mask.label,
-
                          correct.extent = TRUE,
                          flip.y = TRUE,
                          value.modifier = 65535,

R/do.add.percell.R

@@ -5,8 +5,10 @@
 #' @references \url{https://github.com/ImmuneDynamics/Spectre}.
 #'
 #' @import data.table
+#' 
+#' @usage do.add.percell(spatial.dat, percell.dat, roi.col, name = "per.cell")
 #'
-#' @export
+#' @export do.add.percell
 
 do.add.percell <- function(spatial.dat,
                            percell.dat,

R/do.aggregate.R

@@ -27,7 +27,7 @@
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.aggregate
 
 do.aggregate <- function(dat,
                              use.cols,

R/do.align.R

@@ -24,10 +24,11 @@
 #'
 #' @references Ashhurst, T. M., et al. (2019). \url{https://www.ncbi.nlm.nih.gov/pubmed/31077106}
 #'
-#' @examples
-#' aligned.dat <- do.align()
+#' @usage do.align(ref.dat, target.dat, batch.col, align.cols, method = "CytoNorm", 
+#' goal = "mean", nQ = 101, Qmin = 0.01, Qmax = 0.99, write.ref.fcs = TRUE,
+#' write.target.fcs = TRUE, mem.ctrl = TRUE)
 #'
-#' @export
+#' @export do.align
 
 do.align <- function(ref.dat,
                      target.dat,

R/do.asinh.R

@@ -14,8 +14,10 @@
 #' @return A data.table with new columns added, that contain the asinh transformed data.
 #'
 #' @import data.table
+#' 
+#' @usage do.asinh(dat, use.cols, cofactor=5, append.cf=FALSE, reduce.noise=FALSE)
 #'
-#' @export
+#' @export do.asinh
 
 do.asinh <- function(dat,
                      use.cols,

R/do.calculate.area.R

@@ -4,8 +4,12 @@
 #' @param region DEFAULT = NULL. Name of the region mask, if present.
 #'
 #' @import data.table
+#' 
+#' @usage do.calculate.area(dat, region = NULL)
 #'
-#' @export
+#' @export do.calculate.area
+#' 
+#' 
 
 do.calculate.area <- function(dat,
                               region = NULL){

R/do.clip.R

@@ -14,8 +14,10 @@
 #' @return A data.table with new columns added, that contain the clipped data.
 #'
 #' @import data.table
+#' 
+#' @usage do.clip(dat, use.cols, min.value = NULL, max.value = NULL, append.name = '_clipped')
 #'
-#' @export
+#' @export do.clip
 
 do.clip <- function(dat,
                     use.cols,

R/do.combine.cols.R

@@ -13,7 +13,7 @@
 #'
 #' @author Thomas M Ashhurst, \email{thomas.ashhurst@@sydney.edu.au}
 #'
-#' @export
+#' @export do.combine.cols
 
 do.combine.cols <- function(dat, col1, col2, na.rm = TRUE){
 

R/do.create.outlines.R

@@ -6,7 +6,10 @@
 #'
 #' @import data.table
 #'
-#' @export
+#' @usage do.create.outlines(dat, mask.name, method = 'stars')
+#' 
+#' @export do.create.outlines
+#' 
 
 do.create.outlines <- function(dat,
                                mask.name,

R/do.extract.R

@@ -6,8 +6,10 @@
 #' @param fun DEFAULT = 'mean'. Summarisation function.
 #'
 #' @import data.table
+#' 
+#' @usage do.extract(dat, mask, name = "CellData", fun = "mean")
 #'
-#' @export
+#' @export do.extract
 
 do.extract <- function(dat, # spatial.data object
                        mask, # name of the mask being summarised

R/do.extract.cell.dat.R

@@ -2,7 +2,9 @@
 #' 
 #' @import data.table
 #' 
-#' @export
+#' @usage do.extract.cell.dat(spatial.dat, target.dat)
+#' 
+#' @export do.extract.cell.dat
 
 do.extract.cell.dat <- function(spatial.dat,
                                 target.dat){

R/do.filter.R

@@ -15,7 +15,7 @@
 #'
 #' @import data.table
 #' 
-#' @export
+#' @export do.filter
 
 do.filter <- function(dat,
                       use.col,

R/do.filter.percell.R

@@ -2,7 +2,11 @@
 #' 
 #' @import data.table
 #' 
-#' @export
+#' @usage do.filter.percell(spatial.dat, per.cell, to, filter.by,
+#' id.col = "ObjectNumber", x.col = "Location_Center_X", y.col = "Location_Center_Y",
+#' simplify.cp.colname = TRUE, value.modifier = 65535)
+#' 
+#' @export do.filter.percell
 
 do.filter.percell <- function(spatial.dat,
                               per.cell,

R/do.label.from.polygon.R

@@ -2,7 +2,10 @@
 #' 
 #' @import data.table
 #' 
-#' @export
+#' @usage do.label.from.polygon(spatial.dat, cell.dat, mask, labels,
+#' name = "Label", id.col = 'ID', roi.col = 'ROI')
+#' 
+#' @export do.label.from.polygon
 
 do.label.from.polygon <- function(spatial.dat,
                                   cell.dat,

R/do.list.summary.R

@@ -12,7 +12,7 @@
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.list.summary
 
 do.list.summary <- function(dat)
 

R/do.list.switch.R

@@ -19,7 +19,7 @@
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.list.switch
 
 do.list.switch <- function(dat){
 

R/do.logicle.R

@@ -39,14 +39,15 @@
 #'   
 #' @examples 
 #' library(Spectre)
-#' dat = demo.asinh
-#' use.cols = names(dat)[c(2:10)]
-#' dat_lgcl = do.logicle(dat, use.cols = use.cols)
+#' dat = Spectre::demo.clustered[, 2:9]
+#' use.cols = names(dat)
+#' do.logicle(dat, use.cols = use.cols)
 #' 
-#' @author
-#' Givanna Putri, \email{givanna.haryonoputri@@sydney.edu.au}
+#' @usage do.logicle(dat, use.cols, auto.infer.function = TRUE,
+#' linearisation.width = 1.2, max.scale.val = 262144, full.transform.width = 4.5,
+#' additional.negative.range = 0)
 #'   
-#' @export
+#' @export do.logicle
 
 do.logicle <- function(dat, 
                        use.cols,

R/do.merge.files.R

@@ -12,9 +12,15 @@
 #' @references \url{https://sydneycytometry.org.au/spectre}.
 #'
 #' @examples
+#' data.list <- list(
+#' "01_Mock_01" = Spectre::demo.clustered[Sample == '01_Mock_01'],
+#' "02_Mock_02" = Spectre::demo.clustered[Sample == '02_Mock_02']
+#' )
 #' cell.dat <- do.merge.files(dat = data.list, remove.duplicates = TRUE)
+#' 
+#' @usage do.merge.files(dat, remove.duplicates = TRUE)
 #'
-#' @export
+#' @export do.merge.files
 
 do.merge.files <- function(dat, remove.duplicates = TRUE){
 

R/do.pull.data.R

@@ -4,8 +4,10 @@
 #' @param target.dat NO DEFAULT. Dataset to pull.
 #'
 #' @import data.table
+#' 
+#' @usage do.pull.data(spatial.dat, target.dat)
 #'
-#' @export
+#' @export do.pull.data
 
 do.pull.data <- function(spatial.dat,
                           target.dat){

R/do.reorder.R

@@ -15,7 +15,7 @@
 #'
 #' @import data.table
 #' 
-#' @export
+#' @export do.reorder
 
 do.reorder <- function(dat,
                        use.col,

R/do.rescale.R

@@ -9,13 +9,17 @@
 #' @param use.cols NO DEFAULT. Vector of character column names -- these columns will be normalised and added to the data.table as new columns.
 #' @param new.min DEFAULT = 0. The new minimum value.
 #' @param new.max DEFAULT = 1. The new maximum value.
-#' @param append.name DEFAULT = '_rescaled'. Text to be appended to the column names of re-scaled data.
+#' @param append.name DEFAULT = '_rescaled'. 
+#' Text to be appended to the column names of re-scaled data.
 #' 
 #' @return A data.table with new columns added, that contain the re-scaled data.
 #'
 #' @import data.table
+#' 
+#' @usage do.rescale(dat, use.cols, new.min = 0, new.max = 1, 
+#' append.name = '_rescaled')
 #'
-#' @export
+#' @export do.rescale
 
 do.rescale <- function(dat,
                        use.cols,

R/do.stats.R

@@ -1,8 +1,10 @@
 #' Statistical analysis
 #'
 #' @import data.table
+#' 
+#' @usage NULL
 #'
-#' @export
+#' @export do.stats
 
 do.stats <- function(dat,
                      use.cols,

R/do.subsample.R

@@ -14,11 +14,11 @@
 #'
 #' @examples
 #' # Subsample 10,000 cells randomly from the total dataset
-#' sub.dat <- Spectre::do.subsample(dat = Spectre::demo.start,
+#' sub.dat <- Spectre::do.subsample(dat = Spectre::demo.clustered,
 #'                                  targets = 10000)
 #'
 #' # Subsample based on the sample with the smallest number of cells
-#' sub.dat.sample <- Spectre::do.subsample(dat = Spectre::demo.start,
+#' sub.dat.sample <- Spectre::do.subsample(dat = Spectre::demo.clustered,
 #'                                         divide.by = "FileName",
 #'                                         min.per = TRUE)
 #'
@@ -28,7 +28,7 @@
 #'
 #' @import data.table
 #'
-#' @export
+#' @export do.subsample
 
 do.subsample <- function(dat,
                          targets,

R/do.zscore.R

@@ -10,11 +10,11 @@
 #' @return Returns a new data.table with z-score calculations for each selected column
 #'
 #' @examples
-#' res <- do.zscore(dat = Spectre::demo.sum, use.cols = names(Spectre::demo.sum)[c(4:15)])
+#' do.zscore(dat = Spectre::demo.clustered, use.cols = c("NK11", "CD4"))
 #' 
 #' @import data.table
 #'
-#' @export
+#' @export do.zscore
 
 do.zscore <- function(dat,
                       use.cols,

R/make.colour.plot.R

@@ -40,25 +40,16 @@
 #' @usage make.colour.plot(dat, x.axis, y.axis, col.axis)
 #'
 #' @examples
-#' # Load packages
-#' library(Spectre)
-#' package.check()
-#' package.load()
-#'
-#' # Read data
-#' cell.dat <- Spectre::demo.umap
-#' cell.dat <- as.data.table(cell.dat)
-#'
 #' # Draw plot
-#' Spectre::make.colour.plot(dat = cell.dat,
-#'                           x.axis = "UMAP_42_X",
-#'                           y.axis = "UMAP__42Y",
-#'                           col.axis = "BV605.Ly6C")
+#' Spectre::make.colour.plot(dat = Spectre::demo.clustered,
+#'                           x.axis = "UMAP_X",
+#'                           y.axis = "UMAP_Y",
+#'                           col.axis = "CD4_asinh")
 #'
 #'
 #' @author
 #' Thomas M Ashhurst, \email{thomas.ashhurst@@sydney.edu.au}
-#' Givanna Putri, \email{ghar1821@@uni.sydney.edu.au}
+#' Givanna Putri
 #'
 #' @import data.table
 #'